REU student -Amari Lewis P.H.D student- Aidean Sharghi June 6th 2014

Slides:



Advertisements
Similar presentations
Object Recognition with Features Inspired by Visual Cortex T. Serre, L. Wolf, T. Poggio Presented by Andrew C. Gallagher Jan. 25, 2007.
Advertisements

Joshua Fabian Tyler Young James C. Peyton Jones Garrett M. Clayton Integrating the Microsoft Kinect With Simulink: Real-Time Object Tracking Example (
Application of light fields in computer vision AMARI LEWIS – REU STUDENT AIDEAN SHARGHI- PH.D STUENT.
Computer Vision REU Week 2 Adam Kavanaugh. Video Canny Put canny into a loop in order to process multiple frames of a video sequence Put canny into a.
December 2, 2014Computer Vision Lecture 21: Image Understanding 1 Today’s topic is.. Image Understanding.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Handwritten Character Recognition using Hidden Markov Models Quantifying the marginal benefit of exploiting correlations between adjacent characters and.
TESTING DIFFERENT CLASSIFICATION APPROACHES BASED ON FACE RECOGNITION APPLICATION AHMED HELMI ABULILA.
Project 2 SIFT Matching by Hierarchical K-means Quantization
Convolutional Neural Networks for Image Processing with Applications in Mobile Robotics By, Sruthi Moola.
Project 4 Image Search based on BoW model with Inverted File System
Geometric and Radiometric Camera Calibration Shape From Stereo requires geometric knowledge of: –Cameras’ extrinsic parameters, i.e. the geometric relationship.
Tandridge Photographic Society Workshop on PhotoShop Elements presented by Nick Withers.
WEEK 7 Amari Lewis Aidean Sharghi Amari Lewis Aidean Sharghi.
Stereoscopic Imaging for Slow-Moving Autonomous Vehicle By: Alexander Norton Advisor: Dr. Huggins April 26, 2012 Senior Capstone Project Final Presentation.
An Example of Course Project Face Identification.
 The most intelligent device - “Human Brain”.  The machine that revolutionized the whole world – “computer”.  Inefficiencies of the computer has lead.
Definitions Megarays - number of light rays captured by the light field sensor. Plenoptic - camera that uses a mirrolens array to capture 4D light field.
Epipolar geometry Epipolar Plane Baseline Epipoles Epipolar Lines
Sustainable Grading Ralph Westfall, Ph.D. April 2009
Digital Filing A Simple Way to Digitally Centralize and Distribute Documents.
Week5 Amari Lewis Aidean Sharghi. Testing the data for classification Divide the date into TEST and TRAIN data. First the regular.jpeg images Then, the.
APPLICATIONS OF LIGHT FIELDS IN COMPUTER VISION WEEK 2 REU STUDENT: AMARI LEWIS P.H.D STUDENT: AIDEAN SHARGHI.
Week 8 Shelby Thompson. This week: Continued to work with PubFig83 dataset Saved.mat files for each training and testing image.mat files include information.
ELEMENTS OF DESIGN. During the course so far we have not spent a lot of time considering how a photograph should be constructed before the film is exposed.
Creating Better Thumbnails Chris Waclawik. Project Motivation Thumbnails used to quickly select a specific a specific image from a set (when lacking appropriate.
Week 10 Presentation Wesna LaLanne - REU Student Mahdi M. Kalayeh - Mentor.
Feature Matching. Feature Space Outlier Rejection.
Determining the Camera Response Function Short Presentation Dominik Neumann Chair of Pattern Recognition (Computer Science 5) Friedrich-Alexander-University.
CHAPTER 4 THE VISUALIZATION PIPELINE. CONTENTS The focus is on presenting the structure of a complete visualization application, both from a conceptual.
PRESENTATION REU IN COMPUTER VISION 2014 AMARI LEWIS CRCV UNIVERSITY OF CENTRAL FLORIDA.
CT333/CT433 Image Processing and Computer Vision.
WEEK4 RESEARCH Amari Lewis Aidean Sharghi. PREPARING THE DATASET  Cars – 83 samples  3 images for each sample when x=0  7 images for each sample when.
Audio Filtering By: Rudrapratap Jadhav ECE 3551 Microcomputer System.
SIGNATURE RECOGNITION SYSTEM Group Number:10 Group Members: Richa Goyal(y08uc103) Rashmi Singhal(y08uc102)
DATA REPRESENTATION IMAGES GCSE Computing. Learning Objective ■ To understand how an image is represented in Binary ■ To be able to convert a Binary code.
Stereoscopic Imaging for Slow-Moving Autonomous Vehicle By: Alex Norton Advisor: Dr. Huggins February 28, 2012 Senior Project Progress Report Bradley University.
Problem Set 2 Reconstructing a Simpler World COS429 Computer Vision Due October (one week from today)13 th.
Computer Vision Computer Vision based Hole Filling Chad Hantak COMP December 9, 2003.
Project 3 SIFT Matching by Binary SIFT
An Introduction to Digital Image Processing Dr.Amnach Khawne Department of Computer Engineering, KMITL.
Height Estimation from Egocentric Video- Week 1 Dr. Ali Borji Aisha Urooj Khan Jessie Finocchiaro UCF CRCV REU 2016.
CONTENTS:  Introduction.  Face recognition task.  Image preprocessing.  Template Extraction and Normalization.  Template Correlation with image database.
ALICE Offline Week – 22 Oct Visualization of embedding Matevz Tadel, CERN Adam Kisiel, Ohio State University.
Date of download: 9/19/2016 Copyright © 2016 SPIE. All rights reserved. The projection of two coplanar circles. (Color online only.) Figure Legend: From:
Introduction Computational Photography Seminar: EECS 395/495
Year 7 Graphics – 2 Pt. Perspective Drawing
HDF5 for Real-Time and/or Embedded Test Data
Miguel Tavares Coimbra
Visualization of embedding
Query-Focused Video Summarization – Week 1
Automatic Lung Cancer Diagnosis from CT Scans (Week 4)
Outline Announcement Local operations (continued) Linear filters
Pearson Lanka (Pvt) Ltd.
Image Fusion for Context Enhancement and Video Surrealism
By: Kevin Yu Ph.D. in Computer Engineering
Overview Pin-hole model From 3D to 2D Camera projection
Assistive System Progress Report 1
Getting to Know Your Digital Camera
First Homework One week
Mentor: Salman Khokhar
Large Scale Image Deduplication
Question 3 Q3 – read the whole text and answer a question about structure [8 marks] The mark scheme is the same as for Q2 What types of things could you.
Miguel Tavares Coimbra
Predicting Search Targets -Week 2
Amari Lewis Aidean Sharghi
Week 7 Presentation Ngoc Ta Aidean Sharghi
WEEK 4 PRESENTATION NGOC TA AIDEAN SHARGHI.
THE ASSISTIVE SYSTEM SHIFALI KUMAR BISHWO GURUNG JAMES CHOU
Single Image Vignetting Correction
Presentation transcript:

REU student -Amari Lewis P.H.D student- Aidean Sharghi June 6th 2014 Week 3- REU student -Amari Lewis P.H.D student- Aidean Sharghi June 6th 2014

Scale-invariant Representation of Light Field Images for Object Recognition EPFEDL Understanding previous work Studied the buildings on their campus-Switzerland Studied the methods Tried to access their code (dataset)

Implementation of codes using matlab

Task1: study the available light field toolbox for matlab This toolbox was capable with the lytro light field camera, and the raw 3D images Camera calibration, image rectification, colour correction and visualization of lfI See if any of the features would be useful toward the project Lytro software- prospective shifts

Task2: code in matlab extracting the EPI of images (in a simple way)

The output epipolar plane image (EPI) for when y=0 Zoomed in, notice the horizontal lines that will be observing for object recognition

Task3: code in matlab to be able to go through the folders of the dataset and extract the EPI for each image when x is fixed (x=0), and y is fixed (y=0).

EPI when x=0 for bike1

EPI when y=0 bike1

Task4 completing dataset Using the lytro desktop- save the perspective shifts of the image (stack.lfp) Lytro viewer/meltdown software- save each different view separately Each image folder: has its own data (excel) X and Y images separate for EPI extraction